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Thalassia Testudinum) Demographics in South Florida

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Thalassia Testudinum) Demographics in South Florida Limnol. Oceanogr., 46(5), 2001, 1077–1090 ᭧ 2001, by the American Society of Limnology and Oceanography, Inc. Large-scale patterns in seagrass (Thalassia testudinum) demographics in south Florida Bradley J. Peterson1 and James W. Fourqurean Department of Biological Sciences and Southeast Environmental Research Center, Florida International University, University Park, Miami, Florida 33199 Abstract An examination of the population age structure of 118 spatially separated subpopulations of Thalassia testudinum over the extent of the Florida Keys National Marine Sanctuary (FKNMS) during a 2-yr period revealed significant spatial variation in short shoot (SS) demographic characteristics and population dynamics. Shoot age was determined for 12,031 SS. The number of leaf scars on individual shoots was converted to shoot age by use of observed seasonally and spatially variable leaf emergence rates. The yearly mean leaf emergence rate was 0.0295 Ϯ 0.0128 leaves SSϪ1 dϪ1 (Ϯ1 SD), and the median age of counted shoots was ϳ5 yr. A significant relationship between asexual reproductive effort and gross recruitment of SS into the populations (r2 ϭ 0.15, P ϭ 0.001) and between mortality of SS and gross recruitment (r2 ϭ 0.72, P Ͻ 0.001) existed. Thus, the greatest risk of mortality occurred in areas where gross recruitment was highest. The net population growth for T. testudinum within the boundaries of FKNMS was stable (mean ϭϪ0.006 Ϯ 0.089 yrϪ1). However, areas within FKNMS fluctuated between positive and negative net growth rates (Ϫ0.20–0.50 yrϪ1). The power of such large-scale observations is the ability to identify areas of management concern and to frame questions that address the controlling mechanisms that influence these regions of fluctuating population growth. There is a general agreement that anthropogenic alteration Quammen and Onuf 1993). The time course of changes in of the environment has influenced most areas on earth. Shal- seagrass communities caused by eutrophication can be de- low marine ecosystems, including seagrass beds, have been cadal (Fourqurean et al. 1995), resulting in slight year-to- no exception. Despite the recognition of seagrass beds as year changes in the ecosystem. Unfortunately, mapping some of the world’s most productive and valuable ecosys- methods generally suffer from lack of precision because of tems, anthropogenic losses of seagrass beds continue at an spatial and seasonal heterogeneity and sampling error; for alarming rate (Short and Wyllie-Echeverria 1996). Although this reason, mapping techniques generally require long in- anthropogenic seagrass losses have been attributed to many tervals to detect changes and necessitate repeated assess- causes (e.g., dredging, filling, oil spills, and water diversion), ments of areas to ascertain trends. Recently, Duarte et al. most such losses result from human inputs of nutrients and (1994) have advocated the use of reconstructive population sediment into the coastal zone (Short and Wyllie-Echeverria demographic methods to detect population growth trends. 1996). The mechanism of this eutrophication-associated sea- These methods rely on the ability to age individual short grass loss is clear: nutrients cause a shift in plant species shoots (SS) of seagrasses by counting leaf scars and applying dominance from slow-growing seagrasses to faster-growing a plastochron interval (PI, Erickson and Michelini 1957). competitors and, in turn, to epiphytes, macroalgae and phy- The age-frequency distribution of SS is a reflection of re- toplankton (Duarte 1995). The extensive losses of seagrass cruitment and mortality of individual SS to the seagrass pop- beds in the past few decades have focused attention on the ulation. A potential advantage of this approach is that esti- need for fast, reliable methods and tools to evaluate the ex- mates of recruitment and mortality can be obtained from a pansion or decline of seagrass populations. single sampling event. Previous examinations of seagrass Traditionally, seagrass distribution and health have been population growth dynamics that used the PI and reconstruc- assessed by long-term mapping surveys with concurrent es- tion method have consisted primarily of censuses on small timates of biomass and density (e.g., Orth and Moore 1983; spatial scales (0.1–1 km; e.g., Gallegos et al. 1993; Duarte et al. 1994; Durako 1994; Jensen et al. 1996). It is possible, 1 Corresponding author (Petersob@fiu.edu). however, that the reduced sampling effort of the reconstruc- Acknowledgments tive techniques compared with repeated mapping may permit Many people provided field and laboratory support, including regional-scale (hundreds of km), multiyear observations on Carlos Barrosso, Braxton Davis, Cassie Furst, Brian Machovina, seagrass population growth dynamics (e.g., Marba` et al. Leanne Miller, Craig Rose, and Alan Willsie. Special thanks to Cap- 1996). tian Dave Ward and the R/V Magic. The application of these techniques to populations of sea- Funding for this project was provided by grant X994620-94-5 grass SS has not been generally accepted (see Durako and awarded to J.W. Fourqurean, M.J. Durako, and J.C. Zieman from Duarte 1997; Jensen et al. 1997). Jensen et al. (1996) argue the U.S. Environmental Protection Agency as part of the Florida that the required assumptions of constant age-specific mor- Keys National Marine Sanctuary Water Quality Protection Program tality and recruitment rates are untenable. They also express and a Tropical Biology Post-doctoral Fellowship award to B.J.P. from Florida International University. concern that durations of PIs can exhibit considerable spatial This is contribution number 142 of the Southeast Environmental and temporal variation resulting from environmental influ- Research Center and contribution number 31 of the Tropical Biol- ences. Recently, Kaldy et al. (1999) reiterated this concern ogy Program at FIU. and discouraged the use of the plastochron method for con- 1077 1078 Peterson and Fourqurean Fig. 1. Site locations of T. testudinum SS demographic samples for 1996 and 1997. struction of age-frequency distributions of Thalassia testu- extend from the southern tip of Key Biscayne to include the dinum, because this species violated the assumption of equal Dry Tortugas. For the most part, these 9,500 km2 support elapsed time between successive leaf formation. Kaldy et al. seagrass communities; Fourqurean et al. (in press) document (1999) further proposed that this bias in leaf formation mea- that there are Ͼ15,000 km2 of seagrass beds in the south surements leads to the commonly reported seagrass age-fre- Florida region. quency distributions with too few young shoots to account for the older shoots in the populations. Leaf emergence rates—The rate at which new leaves were In this study, the potential of the PI–reconstructive de- produced by SS was determined by leaf punching all SS mographic techniques to regional assessments of the state of within six 0.04 m2 quadrants quarterly at 28 permanent sites seagrass populations was explored. In addition, we attempted within FKNMS in 1996 and 1997 (Fig. 1). These 28 sites to address the concerns raised by critics of the approach and correspond to a concurrent program of water quality moni- reassessed the mathematical models used to derive recruit- toring and were chosen by use of a stratified-random ap- ment and mortality from the age-frequency data. Data from proach, where distance offshore and broad geographic lo- a regional, multiyear, quarterly sampling program was used cations were the strata. After periods of 9–15 d, all SS within to assess spatial and seasonal variability in PIs. Then the the quadrants were harvested. The leaf emergence rate (LER, temporally and spatially variable estimates were applied to in new leaves SSϪ1 dϪ1) was calculated by dividing the num- collections of SS of T. testudinum from throughout the re- ber of new leaves produced in the incubation period by the gion, to estimate population growth parameters for the sub- total number of leaf-punched SS, then dividing by the time populations. Although questions remain about the applica- interval between marking and harvest. Note that LER is the bility of the reconstruction method as a tool for predicting reciprocal of PI, or the number of days between formation change in seagrass populations, we believe that this tech- of successive leaves on a SS (Patriquin 1973; Brouns 1985). nique can be used with caution to examine regional differ- To evaluate the effects of seasonal variation on the estimates ences in population dynamics of seagrass beds. of LER, a sine-wave function (Eq. 1) was fitted to the data on leaf emergence rates acquired from the permanent quar- Methods terly sites by use of nonlinear least-squares regression: LER ϭ LER ϩ Amp[sin(DOY ϩ ␾ )], (1) The Florida Keys National Marine Sanctuary (FKNMS) consists of ϳ9,500 km2 of coastal and oceanic waters that where LER is the mean annual PI, Amp is the amplitude of Variability in seagrass demographics 1079 ␾ the sine wave, DOY is the day of year in radians, and is the estimated number of SS in the youngest age class, Nt is the phase angle. This equation was fitted to LER rather than the number of SS in the tth age class, and M is the per capita to PI because LER approaches 0 in the winter months; hence mortality rate. Every age class contributed to the estimate of PI approached ϱ, which caused instability in the nonlinear M. This calculation assumes (1) age-independent mortality, regression. (2) constant annual SS mortality, and (3) constant annual recruitment rates. All of the SS age classes were used to Population demographics: determining age structure— estimate M instead of the numbers in cohort peaks, as has During the summers of 1996 and 1997, samples of T. tes- been used elsewhere (Durako 1994). We feel that this avoids tudinum to be used in demographic analyses were obtained biasing the estimate of M by only sampling individuals re- from 118 randomly selected sites within the FKNMS (Fig.
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